How does the use of synthetic data impact the fairness and bias of machine learning models?

In recent years, the use of synthetic data has grown in popularity as a means to supplement real-world datasets in various machine learning applications. While it offers benefits such as preserving privacy and expanding diverse data scenarios, concerns have emerged about its implications on model fairness and bias. Does the generation process of synthetic data potentially amplify existing biases present in the original datasets, or can it help mitigate such issues by providing a more balanced data distribution? This question invites exploration into the methodologies for generating synthetic data, the evaluation of its impact on model outcomes, and the ethical considerations data scientists must address when integrating synthetic datasets into their analysis pipelines.